Triple
T33793749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Freiburg |
E866007
|
entity |
| Predicate | universityLanguage |
P54642
|
FINISHED |
| Object | French |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: French | Statement: [Freiburg, universityLanguage, French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: universityLanguage Context triple: [Freiburg, universityLanguage, French]
-
A.
university2
Indicates a relationship where an entity is a university associated with, attended by, or otherwise linked to another entity.
-
B.
university
Indicates that an educational institution of higher learning is associated with or attended by a given entity.
-
C.
campusLanguageEnvironment
Indicates the primary language or mix of languages used for communication and instruction within a campus setting.
-
D.
languageOfInstitutionalContext
chosen
Indicates the language used as the primary medium of communication within an institutional setting or context.
-
E.
academicLanguage
Indicates that the relationship or action is expressed using formal, discipline-specific language typically used in academic or scholarly contexts.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f3498f99f481909cb271f4965a7594 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6ffbad8848190867c2988c0ceb84f |
completed | May 3, 2026, 7:56 a.m. |
| PD | Predicate disambiguation | batch_69f6fc59518081908b0275f47721d561 |
completed | May 3, 2026, 7:42 a.m. |
Created at: May 1, 2026, 1:46 a.m.